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Optimal fiducial points for pulse rate variability analysis from forehead and finger photoplethysmographic signals
Elena Peralta1,2, Jesus Lazaro1,2,3, Raquel Bailon1,2
1Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon,University of Zaragoza, Zaragoza, Spain.
Objective:
The aim of this work is to evaluate and compare five fiducial points for the temporal location of each pulse wave from forehead and finger photoplethysmographic (PPG) pulse wave signals to perform pulse rate variability (PRV) analysis as a surrogate for heart rate variability (HRV) analysis.
Approach:
Forehead and finger PPG signals were recorded during a tilt-table test simultaneously with the electrocardiogram (ECG). Artefacts were detected and removed and five fiducial points were computed: apex, middle-amplitude and foot points of the PPG signal, apex point of the first derivative signal and the intersection point of the tangent to the PPG waveform at the apex of the derivative PPG signal and the tangent to the foot of the PPG pulse, defined as the intersecting tangents method. Pulse period (PP) time interval series were obtained from both PPG signals and compared with the RR intervals obtained from the ECG. HRV and PRV signals were estimated and classical time and frequency domain indices were computed.
Main Results:
The middle-amplitude point of the PPG signal (n M ), the apex point of the first derivative ([Formula: see text]), and the tangent intersection point (n T ) are the most suitable fiducial points for PRV analysis, resulting in the lowest relative errors estimated between PRV and HRV indices and higher correlation coefficients and reliability indices. Statistically significant differences according to the Wilcoxon test between PRV and HRV signals were found for the apex and foot fiducial points of the PPG, as well as the lowest agreement between RR and PP series according to Bland-Altman analysis. Hence, these signals have been considered less accurate for variability analysis. In addition, the relative errors are significantly lower for n M and [Formula: see text] using Friedman statistics with a Bonferroni multiple-comparison test, and we propose that n M is the most accurate fiducial point. Based on our results, forehead PPG seems to provide more reliable information for a PRV assessment than finger PPG.
Significance:
The accuracy of the pulse wave detection depends on the morphology of the PPG. There is therefore a need to widely define the most accurate fiducial point for performing a PRV analysis under non-stationary conditions based on different PPG sensor locations and signal acquisition techniques.
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